auc = c(bayes_auc$.estimate,xgboost_auc$.estimate,decision_tree_auc$.estimate,random_forest_auc$.estimate,svm_auc$.estimate,knn_auc$.estimate,logistic_auc$.estimate)
acc = c(bayes_acc$.estimate,xgboost_acc$.estimate,decision_tree_acc$.estimate,random_forest_acc$.estimate,svm_acc$.estimate,knn_acc$.estimate,logistic_acc$.estimate)
sens = c(bayes_sens$.estimate,xgboost_sens$.estimate,decision_tree_sens$.estimate,random_forest_sens$.estimate,svm_sens$.estimate,knn_sens$.estimate,logistic_sens$.estimate)
spec = c(bayes_spec$.estimate,xgboost_spec$.estimate,decision_tree_spec$.estimate,random_forest_spec$.estimate,svm_spec$.estimate,knn_spec$.estimate,logistic_spec$.estimate)
ppv = c(bayes_ppv$.estimate,xgboost_ppv$.estimate,decision_tree_ppv$.estimate,random_forest_ppv$.estimate,svm_ppv$.estimate,knn_ppv$.estimate,logistic_ppv$.estimate)
f1  = c(bayes_f_meas$.estimate,xgboost_f_meas$.estimate,descrion_f_meas$.estimate,random_forest_f_meas$.estimate,svm_f_meas$.estimate,knn_f_meas$.estimate,logistic_f_meas$.estimate)
name = c("naive_bayes","xgboost","decision_tree","random_forest","svm","knn","logistic")
result_frame = data.frame(name,auc,acc,sens,spec,ppv,f1)

write.csv(result_frame,"./data/all_prediction_result.csv")

conf_mat =c(xgboost_conf_mat,decision_tree_conf_mat,knn_conf_mat,bayes_conf_mat,random_forest_conf_mat,svm_conf_mat,logistic_conf_mat)

for(i in 0:7){
  print(name[i])
  print(conf_mat[i])
}